fix config

main.py

@@ -1,51 +0,0 @@
-import pandas as pd
-import numpy as np
-from pathlib import Path
-import seaborn as sns
-import matplotlib.pyplot as plt
-from sklearn.linear_model import LinearRegression
-from sklearn.metrics import mean_squared_error
-
-
-with open('names') as f_names:
-    names = f_names.read().rstrip('\n').split('\t')
-
-
-cars_train = pd.read_csv('train/train.tsv', sep='\t', names=names)
-
-cars_train = pd.get_dummies(cars_train, columns=['engineType'])
-
-
-y_train = pd.DataFrame(cars_train['price'])
-cars_train.drop('price', inplace=True, axis=1)
-cars_train.drop('brand', inplace=True, axis=1)
-x_train = pd.DataFrame(cars_train)
-
-model = LinearRegression()
-model.fit(x_train, y_train)
-
-names.remove('price')
-
-cars_dev = pd.read_csv('dev-0/in.tsv', sep='\t', names=names)
-with open('dev-0/expected.tsv', 'r') as dev_exp_f:
-    Y_dev = np.array([float(x.rstrip('\n')) for x in dev_exp_f.readlines()])
-
-cars_dev = pd.get_dummies(cars_dev, columns=['engineType'])
-cars_dev.drop('brand', inplace=True, axis=1)
-X_dev = pd.DataFrame(cars_dev)
-
-Y_dev_predicted = model.predict(X_dev)
-print(Y_dev_predicted)
-pd.DataFrame(Y_dev_predicted).to_csv('dev-0/out.tsv', sep='\t', index=False, header=False)
-
-
-cars_test=pd.read_csv('test-A/in.tsv', sep='\t', names=names)
-cars_test = pd.get_dummies(cars_test, columns=['engineType'])
-cars_test.drop('brand', inplace=True, axis=1)
-X_test = pd.DataFrame(cars_test)
-
-Y_test_predicted = model.predict(X_test)
-pd.DataFrame(Y_test_predicted).to_csv('test-A/out.tsv', sep='\t', index=False, header=False)
-
-error = np.sqrt(mean_squared_error(Y_dev, Y_dev_predicted))
-print(error)